Overview

Dataset statistics

Number of variables17
Number of observations26137
Missing cells173199
Missing cells (%)39.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 MiB
Average record size in memory136.0 B

Variable types

Categorical6
Numeric11

Alerts

species has a high cardinality: 600 distinct values High cardinality
abundance_index_units has a high cardinality: 3528 distinct values High cardinality
biomass is highly correlated with biomass_SE and 4 other fieldsHigh correlation
biomass_SE is highly correlated with biomass and 6 other fieldsHigh correlation
abundance is highly correlated with biomass and 4 other fieldsHigh correlation
abundance_SE is highly correlated with biomass and 6 other fieldsHigh correlation
biomass_index is highly correlated with biomass and 7 other fieldsHigh correlation
biomass_index_SE is highly correlated with biomass_SE and 5 other fieldsHigh correlation
abundance_index is highly correlated with biomass and 7 other fieldsHigh correlation
abundance_index_SE is highly correlated with biomass_SE and 5 other fieldsHigh correlation
avg_len is highly correlated with abundance_index and 2 other fieldsHigh correlation
avg_mass is highly correlated with biomass_index and 2 other fieldsHigh correlation
biomass is highly correlated with biomass_SE and 2 other fieldsHigh correlation
biomass_SE is highly correlated with biomass and 3 other fieldsHigh correlation
abundance is highly correlated with abundance_SEHigh correlation
abundance_SE is highly correlated with abundance and 1 other fieldsHigh correlation
biomass_index is highly correlated with biomass and 1 other fieldsHigh correlation
biomass_index_SE is highly correlated with biomass_SE and 2 other fieldsHigh correlation
abundance_index is highly correlated with biomass_SE and 1 other fieldsHigh correlation
abundance_index_SE is highly correlated with biomass and 3 other fieldsHigh correlation
avg_len is highly correlated with avg_massHigh correlation
avg_mass is highly correlated with biomass_index_SE and 1 other fieldsHigh correlation
biomass is highly correlated with biomass_SE and 3 other fieldsHigh correlation
biomass_SE is highly correlated with biomass and 4 other fieldsHigh correlation
abundance is highly correlated with biomass and 3 other fieldsHigh correlation
abundance_SE is highly correlated with biomass and 4 other fieldsHigh correlation
biomass_index is highly correlated with biomass and 4 other fieldsHigh correlation
biomass_index_SE is highly correlated with biomass_SE and 2 other fieldsHigh correlation
abundance_index is highly correlated with abundance and 3 other fieldsHigh correlation
abundance_index_SE is highly correlated with abundance_SE and 1 other fieldsHigh correlation
avg_len is highly correlated with avg_massHigh correlation
avg_mass is highly correlated with biomass_index and 2 other fieldsHigh correlation
ecosystem is highly correlated with season and 2 other fieldsHigh correlation
season is highly correlated with ecosystem and 2 other fieldsHigh correlation
source is highly correlated with ecosystem and 2 other fieldsHigh correlation
agency is highly correlated with ecosystem and 2 other fieldsHigh correlation
ecosystem is highly correlated with avg_len and 3 other fieldsHigh correlation
biomass is highly correlated with biomass_SE and 1 other fieldsHigh correlation
biomass_SE is highly correlated with biomassHigh correlation
abundance is highly correlated with abundance_SEHigh correlation
abundance_SE is highly correlated with abundanceHigh correlation
biomass_index is highly correlated with biomass and 2 other fieldsHigh correlation
biomass_index_SE is highly correlated with biomass_index and 1 other fieldsHigh correlation
abundance_index is highly correlated with abundance_index_SE and 1 other fieldsHigh correlation
abundance_index_SE is highly correlated with abundance_index and 1 other fieldsHigh correlation
avg_len is highly correlated with ecosystem and 3 other fieldsHigh correlation
avg_mass is highly correlated with biomass_index and 1 other fieldsHigh correlation
source is highly correlated with ecosystem and 3 other fieldsHigh correlation
agency is highly correlated with ecosystem and 3 other fieldsHigh correlation
season is highly correlated with ecosystem and 5 other fieldsHigh correlation
biomass has 10416 (39.9%) missing values Missing
biomass_SE has 14810 (56.7%) missing values Missing
abundance has 13522 (51.7%) missing values Missing
abundance_SE has 19546 (74.8%) missing values Missing
biomass_index has 15494 (59.3%) missing values Missing
biomass_index_SE has 19207 (73.5%) missing values Missing
abundance_index has 13514 (51.7%) missing values Missing
abundance_index_units has 1338 (5.1%) missing values Missing
abundance_index_SE has 22198 (84.9%) missing values Missing
avg_len has 22456 (85.9%) missing values Missing
avg_mass has 20698 (79.2%) missing values Missing
biomass_SE is highly skewed (γ1 = 22.38271979) Skewed
biomass_index is highly skewed (γ1 = 20.4014345) Skewed
abundance_index is highly skewed (γ1 = 40.63027914) Skewed
abundance_index_SE is highly skewed (γ1 = 27.53485063) Skewed
biomass has 3370 (12.9%) zeros Zeros
biomass_SE has 2381 (9.1%) zeros Zeros
abundance has 3794 (14.5%) zeros Zeros
abundance_SE has 2140 (8.2%) zeros Zeros
abundance_index_SE has 308 (1.2%) zeros Zeros

Reproduction

Analysis started2022-05-17 18:51:06.182204
Analysis finished2022-05-17 18:51:58.879673
Duration52.7 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

ecosystem
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size204.3 KiB
NB
6766 
NJ
5423 
LIS
3529 
Del
3200 
HR
1323 
Other values (6)
5896 

Length

Max length3
Median length2
Mean length2.407736159
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCB
2nd rowCB
3rd rowCB
4th rowCB
5th rowCB

Common Values

ValueCountFrequency (%)
NB6766
25.9%
NJ5423
20.7%
LIS3529
13.5%
Del3200
12.2%
HR1323
 
5.1%
GB1273
 
4.9%
GoM1214
 
4.6%
SNE1097
 
4.2%
MAB948
 
3.6%
CB695
 
2.7%

Length

2022-05-17T11:51:59.077733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nb6766
25.9%
nj5423
20.7%
lis3529
13.5%
del3200
12.2%
hr1323
 
5.1%
gb1273
 
4.9%
gom1214
 
4.6%
sne1097
 
4.2%
mab948
 
3.6%
cb695
 
2.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

species
Categorical

HIGH CARDINALITY

Distinct600
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size204.3 KiB
Scup
 
450
Winter_flounder
 
447
Summer_flounder
 
438
Red_hake
 
425
Silver_hake
 
418
Other values (595)
23959 

Length

Max length33
Median length14
Mean length13.38898879
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique76 ?
Unique (%)0.3%

Sample

1st rowAmerican_eel_age-1+
2nd rowAmerican_eel_age-1+
3rd rowAmerican_eel_age-1+
4th rowAmerican_eel_age-1+
5th rowAmerican_eel_age-1+

Common Values

ValueCountFrequency (%)
Scup450
 
1.7%
Winter_flounder447
 
1.7%
Summer_flounder438
 
1.7%
Red_hake425
 
1.6%
Silver_hake418
 
1.6%
Atlantic_herring402
 
1.5%
Longfin_squid387
 
1.5%
Windowpane366
 
1.4%
Spiny_dogfish319
 
1.2%
Atlantic_mackerel316
 
1.2%
Other values (590)22169
84.8%

Length

2022-05-17T11:51:59.458095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
winter_flounder535
 
2.0%
scup517
 
2.0%
summer_flounder510
 
2.0%
red_hake492
 
1.9%
silver_hake485
 
1.9%
atlantic_herring465
 
1.8%
longfin_squid449
 
1.7%
windowpane433
 
1.7%
spiny_dogfish384
 
1.5%
atlantic_mackerel368
 
1.4%
Other values (396)21499
82.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

year
Real number (ℝ≥0)

Distinct52
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1993.093584
Minimum1959
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size204.3 KiB
2022-05-17T11:51:59.829900image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1959
5-th percentile1969
Q11986
median1995
Q32002
95-th percentile2008
Maximum2010
Range51
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.7463387
Coefficient of variation (CV)0.005893520904
Kurtosis-0.1056234261
Mean1993.093584
Median Absolute Deviation (MAD)8
Skewness-0.7570171007
Sum52093487
Variance137.9764728
MonotonicityNot monotonic
2022-05-17T11:52:00.252805image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2005937
 
3.6%
2004923
 
3.5%
2002918
 
3.5%
2007910
 
3.5%
2003902
 
3.5%
1997901
 
3.4%
1999895
 
3.4%
1993882
 
3.4%
1998881
 
3.4%
2000878
 
3.4%
Other values (42)17110
65.5%
ValueCountFrequency (%)
195950
 
0.2%
196050
 
0.2%
196150
 
0.2%
196250
 
0.2%
1963114
0.4%
1964118
0.5%
1965120
0.5%
1966220
0.8%
1967237
0.9%
1968247
0.9%
ValueCountFrequency (%)
2010388
1.5%
2009709
2.7%
2008850
3.3%
2007910
3.5%
2006868
3.3%
2005937
3.6%
2004923
3.5%
2003902
3.5%
2002918
3.5%
2001866
3.3%

biomass
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct11579
Distinct (%)73.7%
Missing10416
Missing (%)39.9%
Infinite0
Infinite (%)0.0%
Mean0.4688411398
Minimum0
Maximum91.28441999
Zeros3370
Zeros (%)12.9%
Negative0
Negative (%)0.0%
Memory size204.3 KiB
2022-05-17T11:52:00.747380image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.751953172 × 10-5
median0.007346865014
Q30.159265993
95-th percentile2.295316789
Maximum91.28441999
Range91.28441999
Interquartile range (IQR)0.1592184734

Descriptive statistics

Standard deviation1.962628051
Coefficient of variation (CV)4.186125927
Kurtosis377.6582182
Mean0.4688411398
Median Absolute Deviation (MAD)0.007346865014
Skewness13.63533775
Sum7370.651559
Variance3.851908866
MonotonicityNot monotonic
2022-05-17T11:52:01.189171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03370
 
12.9%
2.750794755 × 10-588
 
0.3%
4.126192133 × 10-577
 
0.3%
8.252384266 × 10-569
 
0.3%
5.50158951 × 10-533
 
0.1%
0.000165047685324
 
0.1%
1.375397378 × 10-517
 
0.1%
0.00012378576414
 
0.1%
0.00024757152814
 
0.1%
2.300525941 × 10-513
 
< 0.1%
Other values (11569)12002
45.9%
(Missing)10416
39.9%
ValueCountFrequency (%)
03370
12.9%
3.087068811 × 10-81
 
< 0.1%
7.735643294 × 10-81
 
< 0.1%
8.252384266 × 10-81
 
< 0.1%
2.193444184 × 10-71
 
< 0.1%
2.200635804 × 10-71
 
< 0.1%
2.750794755 × 10-71
 
< 0.1%
2.859800608 × 10-71
 
< 0.1%
2.875657627 × 10-73
 
< 0.1%
2.945795618 × 10-71
 
< 0.1%
ValueCountFrequency (%)
91.284419991
< 0.1%
41.038527471
< 0.1%
38.209917321
< 0.1%
33.310120281
< 0.1%
32.274378961
< 0.1%
31.380434451
< 0.1%
30.80945921
< 0.1%
30.194845351
< 0.1%
29.562890361
< 0.1%
28.496423361
< 0.1%

biomass_SE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct8522
Distinct (%)75.2%
Missing14810
Missing (%)56.7%
Infinite0
Infinite (%)0.0%
Mean0.05285916293
Minimum0
Maximum14.32161553
Zeros2381
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size204.3 KiB
2022-05-17T11:52:01.665108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.250758253 × 10-5
median0.0007514694421
Q30.01878140347
95-th percentile0.1999103797
Maximum14.32161553
Range14.32161553
Interquartile range (IQR)0.01876889588

Descriptive statistics

Standard deviation0.3037094986
Coefficient of variation (CV)5.745635796
Kurtosis760.5760484
Mean0.05285916293
Median Absolute Deviation (MAD)0.0007514694421
Skewness22.38271979
Sum598.7357385
Variance0.09223945955
MonotonicityNot monotonic
2022-05-17T11:52:02.120633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02381
 
9.1%
1.375397378 × 10-544
 
0.2%
4.126192133 × 10-522
 
0.1%
6.876986888 × 10-621
 
0.1%
2.063096066 × 10-520
 
0.1%
2.750794755 × 10-514
 
0.1%
2.300523758 × 10-513
 
< 0.1%
5.50158951 × 10-611
 
< 0.1%
1.031548033 × 10-510
 
< 0.1%
1.150261879 × 10-59
 
< 0.1%
Other values (8512)8782
33.6%
(Missing)14810
56.7%
ValueCountFrequency (%)
02381
9.1%
1.071849785 × 10-134
 
< 0.1%
1.215363418 × 10-132
 
< 0.1%
1.403380793 × 10-131
 
< 0.1%
1.660502547 × 10-139
 
< 0.1%
1.98468015 × 10-134
 
< 0.1%
2.625485054 × 10-132
 
< 0.1%
3.9693603 × 10-131
 
< 0.1%
2.826338754 × 10-71
 
< 0.1%
2.875654899 × 10-73
 
< 0.1%
ValueCountFrequency (%)
14.321615531
< 0.1%
11.29233861
< 0.1%
9.2943220841
< 0.1%
6.8073815831
< 0.1%
5.7965804171
< 0.1%
5.6042674181
< 0.1%
5.2032313091
< 0.1%
4.9501581331
< 0.1%
4.8903529331
< 0.1%
4.7226865061
< 0.1%

abundance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct5434
Distinct (%)43.1%
Missing13522
Missing (%)51.7%
Infinite0
Infinite (%)0.0%
Mean1194.43357
Minimum0
Maximum316646.6981
Zeros3794
Zeros (%)14.5%
Negative0
Negative (%)0.0%
Memory size204.3 KiB
2022-05-17T11:52:02.526186image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median9.077622692
Q3157.5609961
95-th percentile3452.941403
Maximum316646.6981
Range316646.6981
Interquartile range (IQR)157.5609961

Descriptive statistics

Standard deviation8996.637388
Coefficient of variation (CV)7.532137085
Kurtosis462.2214865
Mean1194.43357
Median Absolute Deviation (MAD)9.077622692
Skewness18.91670563
Sum15067779.49
Variance80939484.29
MonotonicityNot monotonic
2022-05-17T11:52:02.967520image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03794
 
14.5%
0.4126192133142
 
0.5%
0.2750794755138
 
0.5%
2.300525992121
 
0.5%
0.8252384266117
 
0.4%
0.55015895170
 
0.3%
4.60105198465
 
0.2%
6.90157797755
 
0.2%
1.65047685353
 
0.2%
3.30095370640
 
0.2%
Other values (5424)8020
30.7%
(Missing)13522
51.7%
ValueCountFrequency (%)
03794
14.5%
0.2750794755138
 
0.5%
0.27739106777
 
< 0.1%
0.28456497474
 
< 0.1%
0.38511126571
 
< 0.1%
0.4126192133142
 
0.5%
0.45388113462
 
< 0.1%
0.47156481524
 
< 0.1%
0.55015895170
 
0.3%
0.55478213555
 
< 0.1%
ValueCountFrequency (%)
316646.69811
< 0.1%
273622.2611
< 0.1%
267198.73231
< 0.1%
242295.99861
< 0.1%
232764.97551
< 0.1%
221580.03271
< 0.1%
212959.92121
< 0.1%
182884.91481
< 0.1%
182785.57391
< 0.1%
178964.81851
< 0.1%

abundance_SE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct3585
Distinct (%)54.4%
Missing19546
Missing (%)74.8%
Infinite0
Infinite (%)0.0%
Mean441.5375099
Minimum0
Maximum91192.78322
Zeros2140
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size204.3 KiB
2022-05-17T11:52:03.427570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.638671811
Q310.00246736
95-th percentile444.1345777
Maximum91192.78322
Range91192.78322
Interquartile range (IQR)10.00246736

Descriptive statistics

Standard deviation3805.067439
Coefficient of variation (CV)8.617767129
Kurtosis256.0502364
Mean441.5375099
Median Absolute Deviation (MAD)0.638671811
Skewness14.48929502
Sum2910173.728
Variance14478538.22
MonotonicityNot monotonic
2022-05-17T11:52:03.807796image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02140
 
8.2%
2.30052381121
 
0.5%
0.137539737837
 
0.1%
3.21351104935
 
0.1%
4.60104761930
 
0.1%
2.35663414625
 
0.1%
5.0936937825
 
0.1%
2.4155521
 
0.1%
3.88622315420
 
0.1%
0.0916931585118
 
0.1%
Other values (3575)4119
 
15.8%
(Missing)19546
74.8%
ValueCountFrequency (%)
02140
8.2%
1.360283686 × 10-92
 
< 0.1%
1.68722426 × 10-91
 
< 0.1%
1.756118688 × 10-91
 
< 0.1%
4.301594713 × 10-92
 
< 0.1%
6.083373583 × 10-92
 
< 0.1%
0.019550044451
 
< 0.1%
0.020630960661
 
< 0.1%
0.021159959661
 
< 0.1%
0.021308958251
 
< 0.1%
ValueCountFrequency (%)
91192.783221
< 0.1%
89103.246421
< 0.1%
88796.629011
< 0.1%
76057.998191
< 0.1%
74121.986281
< 0.1%
68027.378431
< 0.1%
61106.706121
< 0.1%
58793.671
< 0.1%
52467.151741
< 0.1%
51126.513911
< 0.1%

biomass_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct6899
Distinct (%)64.8%
Missing15494
Missing (%)59.3%
Infinite0
Infinite (%)0.0%
Mean2.29425462
Minimum0
Maximum494.085
Zeros189
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size204.3 KiB
2022-05-17T11:52:04.697914image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0005128205128
Q10.008
median0.09743589744
Q31.052953466
95-th percentile11.06786797
Maximum494.085
Range494.085
Interquartile range (IQR)1.044953466

Descriptive statistics

Standard deviation8.99321745
Coefficient of variation (CV)3.919886386
Kurtosis891.716958
Mean2.29425462
Median Absolute Deviation (MAD)0.0966025641
Skewness20.4014345
Sum24417.75192
Variance80.8779601
MonotonicityNot monotonic
2022-05-17T11:52:05.109253image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0189
 
0.7%
0.0025153
 
0.6%
0.0008333333333131
 
0.5%
0.00125114
 
0.4%
0.00591
 
0.3%
0.00166666666784
 
0.3%
0.000256410256461
 
0.2%
0.00333333333356
 
0.2%
0.000384615384651
 
0.2%
0.007550
 
0.2%
Other values (6889)9663
37.0%
(Missing)15494
59.3%
ValueCountFrequency (%)
0189
0.7%
0.00012345679015
 
< 0.1%
0.0001256
 
< 0.1%
0.000126582278514
 
0.1%
0.000128205128240
 
0.2%
0.000129870129911
 
< 0.1%
0.00013888888897
 
< 0.1%
0.00014084507044
 
< 0.1%
0.00014492753624
 
< 0.1%
0.000147058823510
 
< 0.1%
ValueCountFrequency (%)
494.0851
< 0.1%
177.45588231
< 0.1%
139.57806451
< 0.1%
128.2051
< 0.1%
125.0961
< 0.1%
122.01486111
< 0.1%
121.0161291
< 0.1%
117.09230771
< 0.1%
97.306666671
< 0.1%
94.477768571
< 0.1%

biomass_index_SE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5967
Distinct (%)86.1%
Missing19207
Missing (%)73.5%
Infinite0
Infinite (%)0.0%
Mean0.0390573594
Minimum0
Maximum6.773935202
Zeros250
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size204.3 KiB
2022-05-17T11:52:05.719227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.188158903 × 10-11
Q10.0005801036712
median0.003127155811
Q30.01736225995
95-th percentile0.1552586883
Maximum6.773935202
Range6.773935202
Interquartile range (IQR)0.01678215628

Descriptive statistics

Standard deviation0.1987845804
Coefficient of variation (CV)5.089555041
Kurtosis429.7940162
Mean0.0390573594
Median Absolute Deviation (MAD)0.002998950682
Skewness17.73651733
Sum270.6675006
Variance0.0395153094
MonotonicityNot monotonic
2022-05-17T11:52:06.115487image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0250
 
1.0%
0.000416666666746
 
0.2%
8.401552174 × 10-1234
 
0.1%
6.41025641 × 10-528
 
0.1%
1.188158903 × 10-1127
 
0.1%
0.000166666666724
 
0.1%
4.273504273 × 10-523
 
0.1%
0.000208333333322
 
0.1%
0.0012522
 
0.1%
0.000128205128220
 
0.1%
Other values (5957)6434
 
24.6%
(Missing)19207
73.5%
ValueCountFrequency (%)
0250
1.0%
8.134767913 × 10-133
 
< 0.1%
1.050194022 × 10-121
 
< 0.1%
1.122704634 × 10-121
 
< 0.1%
1.485198629 × 10-121
 
< 0.1%
1.626953583 × 10-122
 
< 0.1%
2.970397258 × 10-121
 
< 0.1%
3.067813096 × 10-124
 
< 0.1%
3.295359883 × 10-121
 
< 0.1%
4.016710209 × 10-123
 
< 0.1%
ValueCountFrequency (%)
6.7739352021
< 0.1%
5.684840261
< 0.1%
4.5916777581
< 0.1%
3.996251
< 0.1%
3.8334744031
< 0.1%
3.5799585531
< 0.1%
3.576164091
< 0.1%
3.406251
< 0.1%
3.1245723081
< 0.1%
2.6173611221
< 0.1%

abundance_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct7969
Distinct (%)63.1%
Missing13514
Missing (%)51.7%
Infinite0
Infinite (%)0.0%
Mean44.68758045
Minimum0
Maximum42762.11704
Zeros200
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size204.3 KiB
2022-05-17T11:52:06.494517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0050307601
Q10.05128205128
median0.4997588037
Q35.061942029
95-th percentile81.82320272
Maximum42762.11704
Range42762.11704
Interquartile range (IQR)5.010659978

Descriptive statistics

Standard deviation610.2820088
Coefficient of variation (CV)13.65663575
Kurtosis2262.15796
Mean44.68758045
Median Absolute Deviation (MAD)0.4884957305
Skewness40.63027914
Sum564091.328
Variance372444.1302
MonotonicityNot monotonic
2022-05-17T11:52:06.828542image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01282051282243
 
0.9%
0200
 
0.8%
0.0125162
 
0.6%
0.008333333333129
 
0.5%
0.025120
 
0.5%
0.03333333333116
 
0.4%
0.0256410256498
 
0.4%
0.182
 
0.3%
0.0126582278572
 
0.3%
0.0570
 
0.3%
Other values (7959)11331
43.4%
(Missing)13514
51.7%
ValueCountFrequency (%)
0200
0.8%
0.00048239266767
 
< 0.1%
0.00050301810869
 
< 0.1%
0.00052192066818
 
< 0.1%
0.00053248136316
 
< 0.1%
0.00053734551325
 
< 0.1%
0.00056306306319
 
< 0.1%
0.00058038305283
 
< 0.1%
0.000628535512311
 
< 0.1%
0.00063011972278
 
< 0.1%
ValueCountFrequency (%)
42762.117041
< 0.1%
22470.948591
< 0.1%
18360.31051
< 0.1%
16398.084851
< 0.1%
15192.374121
< 0.1%
14723.714271
< 0.1%
13337.858561
< 0.1%
11670.824111
< 0.1%
10812.100791
< 0.1%
9433.46241
< 0.1%

abundance_index_units
Categorical

HIGH CARDINALITY
MISSING

Distinct3528
Distinct (%)14.2%
Missing1338
Missing (%)5.1%
Memory size204.3 KiB
nd
20019 
no./5min_tow
 
494
0
 
257
no./km2
 
133
no._/_100_hooks/_hr_normalized
 
68
Other values (3523)
3828 

Length

Max length30
Median length2
Mean length4.101979919
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3417 ?
Unique (%)13.8%

Sample

1st rowno./5min_tow
2nd rowno./5min_tow
3rd rowno./5min_tow
4th rowno./5min_tow
5th rowno./5min_tow

Common Values

ValueCountFrequency (%)
nd20019
76.6%
no./5min_tow494
 
1.9%
0257
 
1.0%
no./km2133
 
0.5%
no._/_100_hooks/_hr_normalized68
 
0.3%
0.006410256410330
 
0.1%
0.004273504273519
 
0.1%
0.01282051282117
 
0.1%
0.01666666666714
 
0.1%
0.003205128205113
 
< 0.1%
Other values (3518)3735
 
14.3%
(Missing)1338
 
5.1%

Length

2022-05-17T11:52:07.183915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nd20019
80.7%
no./5min_tow494
 
2.0%
0257
 
1.0%
no./km2133
 
0.5%
no._/_100_hooks/_hr_normalized68
 
0.3%
0.006410256410330
 
0.1%
0.004273504273519
 
0.1%
0.01282051282117
 
0.1%
0.01666666666714
 
0.1%
0.003205128205113
 
0.1%
Other values (3518)3735
 
15.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

abundance_index_SE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct3319
Distinct (%)84.3%
Missing22198
Missing (%)84.9%
Infinite0
Infinite (%)0.0%
Mean21.34142735
Minimum0
Maximum14000.40451
Zeros308
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size204.3 KiB
2022-05-17T11:52:07.517942image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.00113177172
median0.007630313799
Q30.03295240309
95-th percentile0.8014985442
Maximum14000.40451
Range14000.40451
Interquartile range (IQR)0.03182063137

Descriptive statistics

Standard deviation333.4561763
Coefficient of variation (CV)15.6248301
Kurtosis948.6056645
Mean21.34142735
Median Absolute Deviation (MAD)0.007456491305
Skewness27.53485063
Sum84063.88233
Variance111193.0215
MonotonicityNot monotonic
2022-05-17T11:52:07.870969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0308
 
1.2%
0.00416666666737
 
0.1%
0.00277777777818
 
0.1%
0.012514
 
0.1%
0.01041666714
 
0.1%
0.0062511
 
< 0.1%
0.00208333333311
 
< 0.1%
0.00833333333310
 
< 0.1%
0.0055555555569
 
< 0.1%
0.0030618621799
 
< 0.1%
Other values (3309)3498
 
13.4%
(Missing)22198
84.9%
ValueCountFrequency (%)
0308
1.2%
3.889162936 × 10-121
 
< 0.1%
4.601719644 × 10-122
 
< 0.1%
5.940794515 × 10-125
 
< 0.1%
6.35097648 × 10-121
 
< 0.1%
7.514576711 × 10-121
 
< 0.1%
8.401552174 × 10-127
 
< 0.1%
1.188158903 × 10-111
 
< 0.1%
4.053061268 × 10-112
 
< 0.1%
4.439906167 × 10-111
 
< 0.1%
ValueCountFrequency (%)
14000.404511
< 0.1%
8130.0098111
< 0.1%
6671.5677611
< 0.1%
4924.7235671
< 0.1%
4633.7767641
< 0.1%
3785.5474161
< 0.1%
3760.3644931
< 0.1%
3159.7999681
< 0.1%
2972.3273251
< 0.1%
2916.9333851
< 0.1%

avg_len
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2687
Distinct (%)73.0%
Missing22456
Missing (%)85.9%
Infinite0
Infinite (%)0.0%
Mean18.79520332
Minimum0.002382307683
Maximum785
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size204.3 KiB
2022-05-17T11:52:08.250013image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.002382307683
5-th percentile0.06305555556
Q10.1166666667
median0.1935
Q30.3975
95-th percentile116.0294118
Maximum785
Range784.9976177
Interquartile range (IQR)0.2808333333

Descriptive statistics

Standard deviation54.10553996
Coefficient of variation (CV)2.878688728
Kurtosis40.24989846
Mean18.79520332
Median Absolute Deviation (MAD)0.0935
Skewness5.116379804
Sum69185.14344
Variance2927.409454
MonotonicityNot monotonic
2022-05-17T11:52:08.600046image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0738
 
0.1%
0.0837
 
0.1%
0.0932
 
0.1%
0.1231
 
0.1%
0.1127
 
0.1%
0.1325
 
0.1%
0.1423
 
0.1%
0.1622
 
0.1%
0.119
 
0.1%
0.1919
 
0.1%
Other values (2677)3408
 
13.0%
(Missing)22456
85.9%
ValueCountFrequency (%)
0.0023823076831
 
< 0.1%
0.021
 
< 0.1%
0.02831
 
< 0.1%
0.0310
< 0.1%
0.03031
 
< 0.1%
0.03141
 
< 0.1%
0.03191
 
< 0.1%
0.03231
 
< 0.1%
0.03271
 
< 0.1%
0.03291
 
< 0.1%
ValueCountFrequency (%)
7851
< 0.1%
7601
< 0.1%
5801
< 0.1%
4961
< 0.1%
4751
< 0.1%
472.51
< 0.1%
471.41
< 0.1%
4501
< 0.1%
4401
< 0.1%
4201
< 0.1%

avg_mass
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3871
Distinct (%)71.2%
Missing20698
Missing (%)79.2%
Infinite0
Infinite (%)0.0%
Mean2.124474141
Minimum0
Maximum314.3333333
Zeros12
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size204.3 KiB
2022-05-17T11:52:09.005019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.004246786027
Q10.08499999986
median0.1937499987
Q30.8655911917
95-th percentile9.204249493
Maximum314.3333333
Range314.3333333
Interquartile range (IQR)0.7805911918

Descriptive statistics

Standard deviation10.06394854
Coefficient of variation (CV)4.737148052
Kurtosis389.5064129
Mean2.124474141
Median Absolute Deviation (MAD)0.1787499988
Skewness16.9999233
Sum11555.01485
Variance101.2830602
MonotonicityNot monotonic
2022-05-17T11:52:09.329047image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1529
 
2.0%
0.274
 
0.3%
0.1546
 
0.2%
0.341
 
0.2%
0.00999999977731
 
0.1%
0.2527
 
0.1%
0.424
 
0.1%
0.12524
 
0.1%
0.133333333321
 
0.1%
0.116666666721
 
0.1%
Other values (3861)4601
 
17.6%
(Missing)20698
79.2%
ValueCountFrequency (%)
012
< 0.1%
6.250000297 × 10-51
 
< 0.1%
0.00012500000599
< 0.1%
0.00025000001192
 
< 0.1%
0.00033333332591
 
< 0.1%
0.00033333334923
 
< 0.1%
0.00041025641181
 
< 0.1%
0.00042990615211
 
< 0.1%
0.00045283018821
 
< 0.1%
0.00045833335511
 
< 0.1%
ValueCountFrequency (%)
314.33333331
< 0.1%
256.56666671
< 0.1%
228.041
< 0.1%
208.53333331
< 0.1%
195.951
< 0.1%
170.90714291
< 0.1%
148.251
< 0.1%
147.31
< 0.1%
114.91
< 0.1%
107.44895831
< 0.1%

source
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size204.3 KiB
NJ_Trawl_Survey_Ocean_Stock_Assessment_Program
5423 
NEFSC_Bottom_Trawl_Survey
4532 
Survey
4416 
Long_Island_Sound_Trawl_Survey
3529 
30_ft_bottom_trawl
3200 
Other values (7)
5037 

Length

Max length46
Median length25
Mean length25.78777212
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVIMS_Juvenile_Trawl
2nd rowVIMS_Juvenile_Trawl
3rd rowVIMS_Juvenile_Trawl
4th rowVIMS_Juvenile_Trawl
5th rowVIMS_Juvenile_Trawl

Common Values

ValueCountFrequency (%)
NJ_Trawl_Survey_Ocean_Stock_Assessment_Program5423
20.7%
NEFSC_Bottom_Trawl_Survey4532
17.3%
Survey4416
16.9%
Long_Island_Sound_Trawl_Survey3529
13.5%
30_ft_bottom_trawl3200
12.2%
Hudson_River_Seine_Survey1323
 
5.1%
Trawl_Survey_-_Fox_Island1175
 
4.5%
Trawl_Survey_-_Whale_Rock1175
 
4.5%
CT_Estuarine_Seine_Survey669
 
2.6%
VIMS_Juvenile_Trawl494
 
1.9%
Other values (2)201
 
0.8%

Length

2022-05-17T11:52:09.645079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nj_trawl_survey_ocean_stock_assessment_program5423
20.7%
nefsc_bottom_trawl_survey4532
17.3%
survey4416
16.9%
long_island_sound_trawl_survey3529
13.5%
30_ft_bottom_trawl3200
12.2%
hudson_river_seine_survey1323
 
5.1%
trawl_survey_-_fox_island1175
 
4.5%
trawl_survey_-_whale_rock1175
 
4.5%
ct_estuarine_seine_survey669
 
2.6%
vims_juvenile_trawl494
 
1.9%
Other values (2)201
 
0.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

agency
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size204.3 KiB
NJ_DEP
5423 
NMFS/NEFSC
4532 
RIDEM
4416 
CT_DEP
4198 
Delaware_Division_of_Fish_and_Wildlife
3200 
Other values (3)
4368 

Length

Max length38
Median length6
Mean length10.11952405
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVIMS
2nd rowVIMS
3rd rowVIMS
4th rowVIMS
5th rowVIMS

Common Values

ValueCountFrequency (%)
NJ_DEP5423
20.7%
NMFS/NEFSC4532
17.3%
RIDEM4416
16.9%
CT_DEP4198
16.1%
Delaware_Division_of_Fish_and_Wildlife3200
12.2%
URI2350
9.0%
NY_DEC1323
 
5.1%
VIMS695
 
2.7%

Length

2022-05-17T11:52:09.946308image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-17T11:52:10.228275image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
nj_dep5423
20.7%
nmfs/nefsc4532
17.3%
ridem4416
16.9%
ct_dep4198
16.1%
delaware_division_of_fish_and_wildlife3200
12.2%
uri2350
9.0%
ny_dec1323
 
5.1%
vims695
 
2.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

season
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size204.3 KiB
nd
10082 
Fall
4632 
Spring
3982 
FALL
2410 
SPRING
1850 
Other values (19)
3181 

Length

Max length24
Median length4
Mean length4.237747255
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowApr-Jun
2nd rowApr-Jun
3rd rowApr-Jun
4th rowApr-Jun
5th rowApr-Jun

Common Values

ValueCountFrequency (%)
nd10082
38.6%
Fall4632
17.7%
Spring3982
 
15.2%
FALL2410
 
9.2%
SPRING1850
 
7.1%
Summer/Fall1323
 
5.1%
WINTER1163
 
4.4%
Jan-Apr132
 
0.5%
Sep-Nov123
 
0.5%
Jun-Sep90
 
0.3%
Other values (14)350
 
1.3%

Length

2022-05-17T11:52:10.574845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nd10082
38.6%
fall7042
26.9%
spring5832
22.3%
summer/fall1323
 
5.1%
winter1163
 
4.4%
jan-apr132
 
0.5%
sep-nov123
 
0.5%
jun-sep90
 
0.3%
aug-oct68
 
0.3%
may-jul45
 
0.2%
Other values (12)237
 
0.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-05-17T11:51:51.324930image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:13.659126image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:17.172967image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:20.525838image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:24.368947image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:27.897190image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:31.375079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:35.129992image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:38.605534image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:42.787368image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:47.048358image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:51.666306image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:13.988258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:17.462104image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:20.867865image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:24.679972image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:28.204211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:31.674613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:35.495022image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:38.888558image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:43.150306image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:47.408137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:51.978454image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:14.281446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:17.724121image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:21.607922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:24.970995image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:28.496244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:31.953248image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:35.847051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:39.191576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:43.454832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:47.739275image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:52.341317image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:14.598473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:18.059653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:21.913756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:25.293020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:28.812265image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:32.260747image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:36.202135image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:39.499599image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:43.863219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:48.105830image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:52.716555image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:14.912495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:18.368676image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:22.227774image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:25.724003image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:29.127719image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:32.562765image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:36.530160image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:39.880172image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:44.223175image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:48.470859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:53.054201image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:15.204473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:18.641698image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:22.508800image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:26.075416image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:29.424748image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:32.850967image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:36.823185image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:40.182817image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:44.672800image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:48.810887image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:53.387769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:15.565498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:18.917730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:22.792821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:26.379846image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:29.733818image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:33.176994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:37.126805image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:40.585887image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:45.062075image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:49.076079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:53.786881image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:15.898523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:19.234750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:23.145855image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:26.703146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:30.061080image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:33.562991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:37.433439image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:41.124997image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:45.458648image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:49.345149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:54.141908image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:16.210548image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:19.530767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:23.469885image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:27.023286image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:30.402103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:33.859020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:37.721457image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:41.604767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:45.911719image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:49.689851image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:54.469324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:16.530579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:19.861796image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:23.783904image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:27.322129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:30.762134image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:34.133035image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:38.025490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:42.113516image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:46.362753image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:50.564444image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:54.836868image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:16.841600image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:20.159818image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:24.066925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:27.610164image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:31.065154image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:34.362056image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:38.271513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:42.476347image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:46.701779image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-17T11:51:50.974031image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-05-17T11:52:10.871864image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-17T11:52:11.346900image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-17T11:52:11.826205image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-17T11:52:12.263113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-05-17T11:52:12.612149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-17T11:51:55.506212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-17T11:51:56.654576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-05-17T11:51:57.638276image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-05-17T11:51:58.480390image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

ecosystemspeciesyearbiomassbiomass_SEabundanceabundance_SEbiomass_indexbiomass_index_SEabundance_indexabundance_index_unitsabundance_index_SEavg_lenavg_masssourceagencyseason
0CBAmerican_eel_age-1+1988NaNNaNNaNNaNNaNNaN1.26no./5min_towNaN0.3055NaNVIMS_Juvenile_TrawlVIMSApr-Jun
1CBAmerican_eel_age-1+1989NaNNaNNaNNaNNaNNaN7.93no./5min_towNaN0.2921NaNVIMS_Juvenile_TrawlVIMSApr-Jun
2CBAmerican_eel_age-1+1990NaNNaNNaNNaNNaNNaN4.85no./5min_towNaN0.2701NaNVIMS_Juvenile_TrawlVIMSApr-Jun
3CBAmerican_eel_age-1+1991NaNNaNNaNNaNNaNNaN2.06no./5min_towNaN0.2616NaNVIMS_Juvenile_TrawlVIMSApr-Jun
4CBAmerican_eel_age-1+1992NaNNaNNaNNaNNaNNaN8.29no./5min_towNaN0.2516NaNVIMS_Juvenile_TrawlVIMSApr-Jun
5CBAmerican_eel_age-1+1993NaNNaNNaNNaNNaNNaN3.56no./5min_towNaN0.2431NaNVIMS_Juvenile_TrawlVIMSApr-Jun
6CBAmerican_eel_age-1+1994NaNNaNNaNNaNNaNNaN2.23no./5min_towNaN0.2511NaNVIMS_Juvenile_TrawlVIMSApr-Jun
7CBAmerican_eel_age-1+1995NaNNaNNaNNaNNaNNaN2.69no./5min_towNaN0.2477NaNVIMS_Juvenile_TrawlVIMSApr-Jun
8CBAmerican_eel_age-1+1996NaNNaNNaNNaNNaNNaN2.57no./5min_towNaN0.2578NaNVIMS_Juvenile_TrawlVIMSApr-Jun
9CBAmerican_eel_age-1+1997NaNNaNNaNNaNNaNNaN2.29no./5min_towNaN0.2763NaNVIMS_Juvenile_TrawlVIMSApr-Jun

Last rows

ecosystemspeciesyearbiomassbiomass_SEabundanceabundance_SEbiomass_indexbiomass_index_SEabundance_indexabundance_index_unitsabundance_index_SEavg_lenavg_masssourceagencyseason
26127SNEYellowtail_flounder19980.0439830.010243NaNNaNNaNNaNNaNndNaNNaNNaNNEFSC_Bottom_Trawl_SurveyNMFS/NEFSCnd
26128SNEYellowtail_flounder19990.0471400.006857NaNNaNNaNNaNNaNndNaNNaNNaNNEFSC_Bottom_Trawl_SurveyNMFS/NEFSCnd
26129SNEYellowtail_flounder20000.0510780.008484NaNNaNNaNNaNNaNndNaNNaNNaNNEFSC_Bottom_Trawl_SurveyNMFS/NEFSCnd
26130SNEYellowtail_flounder20010.0500220.005667NaNNaNNaNNaNNaNndNaNNaNNaNNEFSC_Bottom_Trawl_SurveyNMFS/NEFSCnd
26131SNEYellowtail_flounder20020.0525870.009431NaNNaNNaNNaNNaNndNaNNaNNaNNEFSC_Bottom_Trawl_SurveyNMFS/NEFSCnd
26132SNEYellowtail_flounder20030.0463780.004109NaNNaNNaNNaNNaNndNaNNaNNaNNEFSC_Bottom_Trawl_SurveyNMFS/NEFSCnd
26133SNEYellowtail_flounder20040.0289450.003155NaNNaNNaNNaNNaNndNaNNaNNaNNEFSC_Bottom_Trawl_SurveyNMFS/NEFSCnd
26134SNEYellowtail_flounder20050.0215920.004607NaNNaNNaNNaNNaNndNaNNaNNaNNEFSC_Bottom_Trawl_SurveyNMFS/NEFSCnd
26135SNEYellowtail_flounder20060.0499400.007284NaNNaNNaNNaNNaNndNaNNaNNaNNEFSC_Bottom_Trawl_SurveyNMFS/NEFSCnd
26136SNEYellowtail_flounder20070.1229970.004210NaNNaNNaNNaNNaNndNaNNaNNaNNEFSC_Bottom_Trawl_SurveyNMFS/NEFSCnd